Platform Engineer

Job description

We are recruiting a Platform Engineer to join us in our London office. Our mission is to make first contact with superintelligence. We are creating a superlearner that discovers all knowledge from its own experience, from elementary motor skills through to profound intellectual breakthroughs. This superlearning capability - the ability to endlessly discover knowledge and skills, without relying on human data - will be driven by the world’s most powerful reinforcement learning algorithms. The superlearner is expected to rediscover and then transcend the greatest inventions in human history, such as language, science, mathematics and technology. If successful, this will represent a scientific breakthrough of comparable magnitude to Darwin: where his law explained all life, our law will explain and build all intelligence. Role brief As a foundational hire on the platform team, you'll be joining at a point where individual decisions shape how the platform is designed, not just maintained. This isn't a role where you inherit someone else's architecture, you'll be one of the people defining it. We're pushing these learning methods to a scale that hasn’t been tried before and we care deeply about the infrastructure that gets us there. You'll own the infrastructure that the mission depends on; keeping large-scale GPU compute reliable, developer environments fast and the whole platform humming. This is an opportunity for someone who wants to be a part of defining a new paradigm in AI and wants their work to directly speed up the experimentation and progress of our ambitious research. This is a broad role, and the list below is non-exhaustive. You’ll be empowered to shape the role how you’d like in order to best enable our mission. - Kubernetes and containers: manage clusters and deploy internal tools - GPU scheduling at scale: hands-on with tools like KAI scheduler and Kueue to keep thousands of GPUs busy and productive - Making large-scale infrastructure resilient: get to the root of hardware failures and cluster-scale chaos, building the systems that prevent them from recurring - Cloud infrastructure: experience on Google Cloud and other major providers - Observability that actually helps: log management and monitoring with tools like QuickWit, Grafana or Datadog - Developer environments people love: tooling like Tailscale, Workbrew and dev containers that make everyone's day-to-day faster - Clean, well-crafted code: e.g. Python and Rust for tools and infrastructure that are a joy to build on We move fast, give people real ownership and trust engineers to make good judgment calls. We are a team that cares as much about doing this well as doing it fast. If you're interested in joining Ineffable and think your skills would be better suited to a different position please express your interest to the Member of Technical Staff (Research / Engineering) position.